978 lines
25 KiB
TypeScript
978 lines
25 KiB
TypeScript
// utils/backendMapper.ts
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import type {
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AnalysisData,
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AgenticReadinessResult,
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SubFactor,
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TierKey,
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DimensionAnalysis,
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Kpi,
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EconomicModelData,
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} from '../types';
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import type { BackendRawResults } from './apiClient';
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import { BarChartHorizontal, Zap, DollarSign } from 'lucide-react';
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import type { HeatmapDataPoint, CustomerSegment } from '../types';
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function safeNumber(value: any, fallback = 0): number {
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const n = typeof value === 'number' ? value : Number(value);
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return Number.isFinite(n) ? n : fallback;
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}
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function inferTierFromScore(score: number): TierKey {
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if (score >= 8) return 'gold';
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if (score >= 5) return 'silver';
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return 'bronze';
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}
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function computeBalanceScore(values: number[]): number {
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if (!values.length) return 50;
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const mean = values.reduce((a, b) => a + b, 0) / values.length;
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if (mean === 0) return 50;
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const variance =
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values.reduce((acc, v) => acc + Math.pow(v - mean, 2), 0) /
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values.length;
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const std = Math.sqrt(variance);
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const cv = std / mean;
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const rawScore = 100 - cv * 100;
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return Math.max(0, Math.min(100, Math.round(rawScore)));
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}
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function getTopLabel(
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labels: any,
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values: number[]
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): string | undefined {
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if (!Array.isArray(labels) || !labels.length || !values.length) {
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return undefined;
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}
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const len = Math.min(labels.length, values.length);
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let maxIdx = 0;
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let maxVal = values[0];
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for (let i = 1; i < len; i++) {
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if (values[i] > maxVal) {
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maxVal = values[i];
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maxIdx = i;
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}
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}
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return String(labels[maxIdx]);
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}
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// ==== Helpers para distribución horaria (desde heatmap_24x7) ====
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function computeHourlyFromHeatmap(heatmap24x7: any): number[] {
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if (!Array.isArray(heatmap24x7) || !heatmap24x7.length) {
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return [];
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}
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const hours = Array(24).fill(0);
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for (const day of heatmap24x7) {
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for (let h = 0; h < 24; h++) {
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const key = String(h);
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const v = safeNumber(day?.[key], 0);
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hours[h] += v;
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}
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}
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return hours;
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}
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function calcOffHoursPct(hourly: number[]): number {
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const total = hourly.reduce((a, b) => a + b, 0);
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if (!total) return 0;
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const offHours =
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hourly.slice(0, 8).reduce((a, b) => a + b, 0) +
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hourly.slice(19, 24).reduce((a, b) => a + b, 0);
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return offHours / total;
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}
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function findPeakHours(hourly: number[]): number[] {
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if (!hourly.length) return [];
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const sorted = [...hourly].sort((a, b) => b - a);
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const threshold = sorted[Math.min(2, sorted.length - 1)] || 0;
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return hourly
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.map((val, idx) => (val >= threshold ? idx : -1))
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.filter((idx) => idx !== -1);
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}
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// ==== Agentic readiness ====
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function mapAgenticReadiness(
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raw: any,
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fallbackTier: TierKey
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): AgenticReadinessResult | undefined {
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const ar = raw?.agentic_readiness?.agentic_readiness;
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if (!ar) {
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return undefined;
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}
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const score = safeNumber(ar.final_score, 5);
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const classification = ar.classification || {};
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const weights = ar.weights || {};
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const sub_scores = ar.sub_scores || {};
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const baseWeights = weights.base_weights || {};
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const normalized = weights.normalized_weights || {};
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const subFactors: SubFactor[] = Object.entries(sub_scores).map(
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([key, value]: [string, any]) => {
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const subScore = safeNumber(value?.score, 0);
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const weight =
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safeNumber(normalized?.[key], NaN) ||
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safeNumber(baseWeights?.[key], 0);
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return {
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name: key,
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displayName: key.replace(/_/g, ' '),
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score: subScore,
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weight,
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description:
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value?.reason ||
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value?.details?.description ||
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'Sub-factor calculado a partir de KPIs agregados.',
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details: value?.details || {},
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};
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}
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);
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const tier = inferTierFromScore(score) || fallbackTier;
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const interpretation =
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classification?.description ||
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`Puntuación de preparación agentic: ${score.toFixed(1)}/10`;
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const computedCount = Object.values(sub_scores).filter(
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(s: any) => s?.computed
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).length;
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const totalCount = Object.keys(sub_scores).length || 1;
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const ratio = computedCount / totalCount;
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const confidence: AgenticReadinessResult['confidence'] =
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ratio >= 0.75 ? 'high' : ratio >= 0.4 ? 'medium' : 'low';
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return {
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score,
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sub_factors: subFactors,
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tier,
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confidence,
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interpretation,
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};
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}
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// ==== Volumetría (dimensión + KPIs) ====
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function buildVolumetryDimension(
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raw: BackendRawResults
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): { dimension?: DimensionAnalysis; extraKpis: Kpi[] } {
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const volumetry = raw?.volumetry;
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const volumeByChannel = volumetry?.volume_by_channel;
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const volumeBySkill = volumetry?.volume_by_skill;
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const channelValues: number[] = Array.isArray(volumeByChannel?.values)
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? volumeByChannel.values.map((v: any) => safeNumber(v, 0))
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: [];
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const rawSkillLabels =
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volumeBySkill?.labels ??
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volumeBySkill?.skills ??
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volumeBySkill?.skill_names ??
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[];
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const skillLabels: string[] = Array.isArray(rawSkillLabels)
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? rawSkillLabels.map((s: any) => String(s))
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: [];
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const skillValues: number[] = Array.isArray(volumeBySkill?.values)
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? volumeBySkill.values.map((v: any) => safeNumber(v, 0))
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: [];
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const totalVolumeChannels = channelValues.reduce((a, b) => a + b, 0);
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const totalVolumeSkills = skillValues.reduce((a, b) => a + b, 0);
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const totalVolume =
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totalVolumeChannels || totalVolumeSkills || 0;
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const numChannels = Array.isArray(volumeByChannel?.labels)
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? volumeByChannel.labels.length
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: 0;
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const numSkills = skillLabels.length;
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const topChannel = getTopLabel(volumeByChannel?.labels, channelValues);
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const topSkill = getTopLabel(skillLabels, skillValues);
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// Heatmap 24x7 -> distribución horaria
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const heatmap24x7 = volumetry?.heatmap_24x7;
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const hourly = computeHourlyFromHeatmap(heatmap24x7);
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const offHoursPct = hourly.length ? calcOffHoursPct(hourly) : 0;
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const peakHours = hourly.length ? findPeakHours(hourly) : [];
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console.log('📊 Volumetría backend (mapper):', {
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volumetry,
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volumeByChannel,
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volumeBySkill,
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totalVolume,
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numChannels,
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numSkills,
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skillLabels,
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skillValues,
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hourly,
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offHoursPct,
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peakHours,
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});
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const extraKpis: Kpi[] = [];
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if (totalVolume > 0) {
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extraKpis.push({
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label: 'Volumen total (backend)',
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value: totalVolume.toLocaleString('es-ES'),
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});
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}
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if (numChannels > 0) {
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extraKpis.push({
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label: 'Canales analizados',
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value: String(numChannels),
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});
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}
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if (numSkills > 0) {
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extraKpis.push({
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label: 'Skills analizadas',
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value: String(numSkills),
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});
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extraKpis.push({
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label: 'Skills (backend)',
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value: skillLabels.join(', '),
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});
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} else {
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extraKpis.push({
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label: 'Skills (backend)',
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value: 'N/A',
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});
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}
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if (topChannel) {
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extraKpis.push({
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label: 'Canal principal',
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value: topChannel,
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});
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}
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if (topSkill) {
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extraKpis.push({
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label: 'Skill principal',
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value: topSkill,
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});
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}
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if (!totalVolume) {
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return { dimension: undefined, extraKpis };
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}
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const summaryParts: string[] = [];
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summaryParts.push(
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`Se han analizado aproximadamente ${totalVolume.toLocaleString(
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'es-ES'
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)} interacciones mensuales.`
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);
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if (numChannels > 0) {
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summaryParts.push(
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`El tráfico se reparte en ${numChannels} canales${
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topChannel ? `, destacando ${topChannel} como el canal con mayor volumen` : ''
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}.`
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);
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}
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if (numSkills > 0) {
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const skillsList =
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skillLabels.length > 0 ? skillLabels.join(', ') : undefined;
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summaryParts.push(
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`Se han identificado ${numSkills} skills${
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skillsList ? ` (${skillsList})` : ''
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}${
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topSkill ? `, siendo ${topSkill} la de mayor carga` : ''
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}.`
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);
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}
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const dimension: DimensionAnalysis = {
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id: 'volumetry_distribution',
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name: 'volumetry_distribution',
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title: 'Volumetría y distribución de demanda',
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score: computeBalanceScore(
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skillValues.length ? skillValues : channelValues
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),
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percentile: undefined,
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summary: summaryParts.join(' '),
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kpi: {
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label: 'Interacciones mensuales (backend)',
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value: totalVolume.toLocaleString('es-ES'),
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},
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icon: BarChartHorizontal,
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distribution_data: hourly.length
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? {
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hourly,
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off_hours_pct: offHoursPct,
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peak_hours: peakHours,
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}
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: undefined,
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};
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return { dimension, extraKpis };
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}
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// ==== Performance (operational_performance) ====
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function buildPerformanceDimension(
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raw: BackendRawResults
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): DimensionAnalysis | undefined {
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const op = raw?.operational_performance;
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if (!op) return undefined;
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const perfScore0_10 = safeNumber(op.performance_score?.score, NaN);
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if (!Number.isFinite(perfScore0_10)) return undefined;
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const score = Math.max(
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0,
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Math.min(100, Math.round(perfScore0_10 * 10))
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);
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const ahtP50 = safeNumber(op.aht_distribution?.p50, 0);
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const ahtP90 = safeNumber(op.aht_distribution?.p90, 0);
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const ratio = safeNumber(op.aht_distribution?.p90_p50_ratio, 0);
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const escRate = safeNumber(op.escalation_rate, 0);
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let summary = `El AHT mediano se sitúa en ${Math.round(
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ahtP50
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)} segundos, con un P90 de ${Math.round(
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ahtP90
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)}s (ratio P90/P50 ≈ ${ratio.toFixed(
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2
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)}) y una tasa de escalación del ${escRate.toFixed(
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1
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)}%. `;
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if (score >= 80) {
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summary +=
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'El rendimiento operativo es sólido y se encuentra claramente por encima de los umbrales objetivo.';
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} else if (score >= 60) {
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summary +=
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'El rendimiento es aceptable pero existen oportunidades claras de optimización en algunos flujos.';
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} else {
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summary +=
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'El rendimiento operativo está por debajo del nivel deseado y requiere un plan de mejora específico.';
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}
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const kpi: Kpi = {
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label: 'AHT mediano (P50)',
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value: ahtP50 ? `${Math.round(ahtP50)}s` : 'N/D',
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};
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const dimension: DimensionAnalysis = {
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id: 'performance',
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name: 'performance',
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title: 'Rendimiento operativo',
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score,
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percentile: undefined,
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summary,
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kpi,
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icon: Zap,
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};
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return dimension;
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}
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// ==== Economía y costes (economy_costs) ====
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function buildEconomicModel(raw: BackendRawResults): EconomicModelData {
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const econ = raw?.economy_costs;
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const cost = econ?.cost_breakdown || {};
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const totalAnnual = safeNumber(cost.total_annual, 0);
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const laborAnnual = safeNumber(cost.labor_annual, 0);
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const overheadAnnual = safeNumber(cost.overhead_annual, 0);
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const techAnnual = safeNumber(cost.tech_annual, 0);
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const potential = econ?.potential_savings || {};
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const annualSavings = safeNumber(potential.annual_savings, 0);
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const currentAnnualCost =
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totalAnnual || laborAnnual + overheadAnnual + techAnnual || 0;
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const futureAnnualCost = currentAnnualCost - annualSavings;
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let initialInvestment = 0;
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let paybackMonths = 0;
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let roi3yr = 0;
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if (annualSavings > 0 && currentAnnualCost > 0) {
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initialInvestment = Math.round(currentAnnualCost * 0.15);
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paybackMonths = Math.ceil(
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(initialInvestment / annualSavings) * 12
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);
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roi3yr =
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((annualSavings * 3 - initialInvestment) /
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initialInvestment) *
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100;
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}
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const savingsBreakdown = annualSavings
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? [
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{
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category: 'Ineficiencias operativas (AHT, escalaciones)',
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amount: Math.round(annualSavings * 0.5),
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percentage: 50,
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},
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{
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category: 'Automatización de volumen repetitivo',
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amount: Math.round(annualSavings * 0.3),
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percentage: 30,
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},
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{
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category: 'Otros beneficios (calidad, CX)',
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amount: Math.round(annualSavings * 0.2),
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percentage: 20,
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},
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]
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: [];
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const costBreakdown = currentAnnualCost
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? [
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{
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category: 'Coste laboral',
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amount: laborAnnual,
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percentage: Math.round(
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(laborAnnual / currentAnnualCost) * 100
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),
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},
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{
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category: 'Overhead',
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amount: overheadAnnual,
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percentage: Math.round(
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(overheadAnnual / currentAnnualCost) * 100
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),
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},
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{
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category: 'Tecnología',
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amount: techAnnual,
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percentage: Math.round(
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(techAnnual / currentAnnualCost) * 100
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),
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},
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]
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: [];
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return {
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currentAnnualCost,
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futureAnnualCost,
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annualSavings,
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initialInvestment,
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paybackMonths,
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roi3yr: parseFloat(roi3yr.toFixed(1)),
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savingsBreakdown,
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npv: 0,
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costBreakdown,
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};
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}
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function buildEconomyDimension(
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raw: BackendRawResults
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): DimensionAnalysis | undefined {
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const econ = raw?.economy_costs;
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if (!econ) return undefined;
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const cost = econ.cost_breakdown || {};
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const totalAnnual = safeNumber(cost.total_annual, 0);
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const potential = econ.potential_savings || {};
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const annualSavings = safeNumber(potential.annual_savings, 0);
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if (!totalAnnual && !annualSavings) return undefined;
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const savingsPct = totalAnnual
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? (annualSavings / totalAnnual) * 100
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: 0;
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let summary = `El coste anual estimado de la operación es de aproximadamente €${totalAnnual.toFixed(
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2
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)}. `;
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if (annualSavings > 0) {
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summary += `El ahorro potencial anual asociado a la estrategia agentic se sitúa en torno a €${annualSavings.toFixed(
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2
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)}, equivalente a ~${savingsPct.toFixed(1)}% del coste actual.`;
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} else {
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summary +=
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'Todavía no se dispone de una estimación robusta de ahorro potencial.';
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}
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const score =
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totalAnnual && annualSavings
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? Math.max(0, Math.min(100, Math.round(savingsPct)))
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: 50;
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const dimension: DimensionAnalysis = {
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id: 'economy',
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name: 'economy',
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title: 'Economía y costes',
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score,
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percentile: undefined,
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summary,
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kpi: {
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label: 'Coste anual actual',
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value: totalAnnual
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? `€${totalAnnual.toFixed(0)}`
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: 'N/D',
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},
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icon: DollarSign,
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};
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return dimension;
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}
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/**
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* Transforma el JSON del backend (results) al AnalysisData
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* que espera el frontend.
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*/
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export function mapBackendResultsToAnalysisData(
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raw: BackendRawResults,
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tierFromFrontend?: TierKey
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): AnalysisData {
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const volumetry = raw?.volumetry;
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const volumeByChannel = volumetry?.volume_by_channel;
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const volumeBySkill = volumetry?.volume_by_skill;
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const channelValues: number[] = Array.isArray(volumeByChannel?.values)
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? volumeByChannel.values.map((v: any) => safeNumber(v, 0))
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: [];
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const skillValues: number[] = Array.isArray(volumeBySkill?.values)
|
|
? volumeBySkill.values.map((v: any) => safeNumber(v, 0))
|
|
: [];
|
|
|
|
const totalVolumeChannels = channelValues.reduce((a, b) => a + b, 0);
|
|
const totalVolumeSkills = skillValues.reduce((a, b) => a + b, 0);
|
|
const totalVolume =
|
|
totalVolumeChannels || totalVolumeSkills || 0;
|
|
|
|
const numChannels = Array.isArray(volumeByChannel?.labels)
|
|
? volumeByChannel.labels.length
|
|
: 0;
|
|
const numSkills = Array.isArray(volumeBySkill?.labels)
|
|
? volumeBySkill.labels.length
|
|
: 0;
|
|
|
|
// Agentic readiness
|
|
const agenticReadiness = mapAgenticReadiness(
|
|
raw,
|
|
tierFromFrontend || 'silver'
|
|
);
|
|
const arScore = agenticReadiness?.score ?? 5;
|
|
const overallHealthScore = Math.max(
|
|
0,
|
|
Math.min(100, Math.round(arScore * 10))
|
|
);
|
|
|
|
// Dimensiones
|
|
const { dimension: volumetryDimension, extraKpis } =
|
|
buildVolumetryDimension(raw);
|
|
const performanceDimension = buildPerformanceDimension(raw);
|
|
const economyDimension = buildEconomyDimension(raw);
|
|
|
|
const dimensions: DimensionAnalysis[] = [];
|
|
if (volumetryDimension) dimensions.push(volumetryDimension);
|
|
if (performanceDimension) dimensions.push(performanceDimension);
|
|
if (economyDimension) dimensions.push(economyDimension);
|
|
|
|
const op = raw?.operational_performance;
|
|
const cs = raw?.customer_satisfaction;
|
|
|
|
// FCR: viene ya como porcentaje 0-100
|
|
const fcrPctRaw = safeNumber(op?.fcr_rate, NaN);
|
|
const fcrPct =
|
|
Number.isFinite(fcrPctRaw) && fcrPctRaw >= 0
|
|
? Math.min(100, Math.max(0, fcrPctRaw))
|
|
: undefined;
|
|
|
|
const csatAvg = computeCsatAverage(cs);
|
|
|
|
// CSAT global (opcional)
|
|
const csatGlobalRaw = safeNumber(cs?.csat_global, NaN);
|
|
const csatGlobal =
|
|
Number.isFinite(csatGlobalRaw) && csatGlobalRaw > 0
|
|
? csatGlobalRaw
|
|
: undefined;
|
|
|
|
|
|
// KPIs de resumen (los 4 primeros son los que se ven en "Métricas de Contacto")
|
|
const summaryKpis: Kpi[] = [];
|
|
|
|
// 1) Interacciones Totales (volumen backend)
|
|
summaryKpis.push({
|
|
label: 'Interacciones Totales',
|
|
value:
|
|
totalVolume > 0
|
|
? totalVolume.toLocaleString('es-ES')
|
|
: 'N/D',
|
|
});
|
|
|
|
// 2) AHT Promedio (P50 de distribución de AHT)
|
|
const ahtP50 = safeNumber(op?.aht_distribution?.p50, 0);
|
|
summaryKpis.push({
|
|
label: 'AHT Promedio',
|
|
value: ahtP50
|
|
? `${Math.round(ahtP50)}s`
|
|
: 'N/D',
|
|
});
|
|
|
|
// 3) Tasa FCR
|
|
summaryKpis.push({
|
|
label: 'Tasa FCR',
|
|
value:
|
|
fcrPct !== undefined
|
|
? `${Math.round(fcrPct)}%`
|
|
: 'N/D',
|
|
});
|
|
|
|
// 4) CSAT
|
|
summaryKpis.push({
|
|
label: 'CSAT',
|
|
value:
|
|
csatGlobal !== undefined
|
|
? `${csatGlobal.toFixed(1)}/5`
|
|
: 'N/D',
|
|
});
|
|
|
|
// --- KPIs adicionales, usados en otras secciones ---
|
|
|
|
if (numChannels > 0) {
|
|
summaryKpis.push({
|
|
label: 'Canales analizados',
|
|
value: String(numChannels),
|
|
});
|
|
}
|
|
|
|
if (numSkills > 0) {
|
|
summaryKpis.push({
|
|
label: 'Skills analizadas',
|
|
value: String(numSkills),
|
|
});
|
|
}
|
|
|
|
summaryKpis.push({
|
|
label: 'Agentic readiness',
|
|
value: `${arScore.toFixed(1)}/10`,
|
|
});
|
|
|
|
// KPIs de economía (backend)
|
|
const econ = raw?.economy_costs;
|
|
const totalAnnual = safeNumber(
|
|
econ?.cost_breakdown?.total_annual,
|
|
0
|
|
);
|
|
const annualSavings = safeNumber(
|
|
econ?.potential_savings?.annual_savings,
|
|
0
|
|
);
|
|
|
|
if (totalAnnual) {
|
|
summaryKpis.push({
|
|
label: 'Coste anual actual (backend)',
|
|
value: `€${totalAnnual.toFixed(0)}`,
|
|
});
|
|
}
|
|
if (annualSavings) {
|
|
summaryKpis.push({
|
|
label: 'Ahorro potencial anual (backend)',
|
|
value: `€${annualSavings.toFixed(0)}`,
|
|
});
|
|
}
|
|
|
|
const mergedKpis: Kpi[] = [...summaryKpis, ...extraKpis];
|
|
|
|
const economicModel = buildEconomicModel(raw);
|
|
|
|
return {
|
|
tier: tierFromFrontend,
|
|
overallHealthScore,
|
|
summaryKpis: mergedKpis,
|
|
dimensions,
|
|
heatmapData: [], // el heatmap por skill lo seguimos generando en el front
|
|
findings: [],
|
|
recommendations: [],
|
|
opportunities: [],
|
|
roadmap: [],
|
|
economicModel,
|
|
benchmarkData: [],
|
|
agenticReadiness,
|
|
staticConfig: undefined,
|
|
source: 'backend',
|
|
};
|
|
}
|
|
|
|
export function buildHeatmapFromBackend(
|
|
raw: BackendRawResults,
|
|
costPerHour: number,
|
|
avgCsat: number,
|
|
segmentMapping?: {
|
|
high_value_queues: string[];
|
|
medium_value_queues: string[];
|
|
low_value_queues: string[];
|
|
}
|
|
): HeatmapDataPoint[] {
|
|
const volumetry = raw?.volumetry;
|
|
const volumeBySkill = volumetry?.volume_by_skill;
|
|
|
|
const rawSkillLabels =
|
|
volumeBySkill?.labels ??
|
|
volumeBySkill?.skills ??
|
|
volumeBySkill?.skill_names ??
|
|
[];
|
|
|
|
const skillLabels: string[] = Array.isArray(rawSkillLabels)
|
|
? rawSkillLabels.map((s: any) => String(s))
|
|
: [];
|
|
|
|
const skillVolumes: number[] = Array.isArray(volumeBySkill?.values)
|
|
? volumeBySkill.values.map((v: any) => safeNumber(v, 0))
|
|
: [];
|
|
|
|
const op = raw?.operational_performance;
|
|
const econ = raw?.economy_costs;
|
|
const cs = raw?.customer_satisfaction;
|
|
|
|
const talkHoldAcwBySkill = Array.isArray(
|
|
op?.talk_hold_acw_p50_by_skill
|
|
)
|
|
? op.talk_hold_acw_p50_by_skill
|
|
: [];
|
|
|
|
const globalEscalation = safeNumber(op?.escalation_rate, 0);
|
|
const globalFcrPct = Math.max(
|
|
0,
|
|
Math.min(100, 100 - globalEscalation)
|
|
);
|
|
|
|
const csatGlobalRaw = safeNumber(cs?.csat_global, NaN);
|
|
const csatGlobal =
|
|
Number.isFinite(csatGlobalRaw) && csatGlobalRaw > 0
|
|
? csatGlobalRaw
|
|
: undefined;
|
|
const csatMetric0_100 = csatGlobal
|
|
? Math.max(
|
|
0,
|
|
Math.min(100, Math.round((csatGlobal / 5) * 100))
|
|
)
|
|
: 0;
|
|
|
|
const ineffBySkill = Array.isArray(
|
|
econ?.inefficiency_cost_by_skill_channel
|
|
)
|
|
? econ.inefficiency_cost_by_skill_channel
|
|
: [];
|
|
|
|
const COST_PER_SECOND = costPerHour / 3600;
|
|
|
|
if (!skillLabels.length) return [];
|
|
|
|
// Para normalizar la repetitividad según volumen
|
|
const volumesForNorm = skillVolumes.filter((v) => v > 0);
|
|
const minVol =
|
|
volumesForNorm.length > 0
|
|
? Math.min(...volumesForNorm)
|
|
: 0;
|
|
const maxVol =
|
|
volumesForNorm.length > 0
|
|
? Math.max(...volumesForNorm)
|
|
: 0;
|
|
|
|
const heatmap: HeatmapDataPoint[] = [];
|
|
|
|
for (let i = 0; i < skillLabels.length; i++) {
|
|
const skill = skillLabels[i];
|
|
const volume = safeNumber(skillVolumes[i], 0);
|
|
|
|
const talkHold = talkHoldAcwBySkill[i] || {};
|
|
const talk_p50 = safeNumber(talkHold.talk_p50, 0);
|
|
const hold_p50 = safeNumber(talkHold.hold_p50, 0);
|
|
const acw_p50 = safeNumber(talkHold.acw_p50, 0);
|
|
|
|
const aht_mean = talk_p50 + hold_p50 + acw_p50;
|
|
|
|
// Coste anual aproximado
|
|
const annual_volume = volume * 12;
|
|
const annual_cost = Math.round(
|
|
annual_volume * aht_mean * COST_PER_SECOND
|
|
);
|
|
|
|
const ineff = ineffBySkill[i] || {};
|
|
const aht_p50_backend = safeNumber(ineff.aht_p50, aht_mean);
|
|
const aht_p90_backend = safeNumber(ineff.aht_p90, aht_mean);
|
|
|
|
// Variabilidad proxy: aproximamos CV a partir de P90-P50
|
|
let cv_aht = 0;
|
|
if (aht_p50_backend > 0) {
|
|
cv_aht =
|
|
(aht_p90_backend - aht_p50_backend) / aht_p50_backend;
|
|
}
|
|
|
|
// Dimensiones agentic similares a las que tenías en generateHeatmapData,
|
|
// pero usando valores reales en lugar de aleatorios.
|
|
|
|
// 1) Predictibilidad (menor CV => mayor puntuación)
|
|
const predictability_score = Math.max(
|
|
0,
|
|
Math.min(
|
|
10,
|
|
10 - ((cv_aht - 0.3) / 1.2) * 10
|
|
)
|
|
);
|
|
|
|
// 2) Complejidad inversa (usamos la tasa global de escalación como proxy)
|
|
const transfer_rate = globalEscalation; // %
|
|
const complexity_inverse_score = Math.max(
|
|
0,
|
|
Math.min(
|
|
10,
|
|
10 - ((transfer_rate / 100 - 0.05) / 0.25) * 10
|
|
)
|
|
);
|
|
|
|
// 3) Repetitividad (según volumen relativo)
|
|
let repetitivity_score = 5;
|
|
if (maxVol > minVol && volume > 0) {
|
|
repetitivity_score =
|
|
((volume - minVol) / (maxVol - minVol)) * 10;
|
|
} else if (volume === 0) {
|
|
repetitivity_score = 0;
|
|
}
|
|
|
|
const agentic_readiness_score =
|
|
predictability_score * 0.4 +
|
|
complexity_inverse_score * 0.35 +
|
|
repetitivity_score * 0.25;
|
|
|
|
let readiness_category:
|
|
| 'automate_now'
|
|
| 'assist_copilot'
|
|
| 'optimize_first';
|
|
if (agentic_readiness_score >= 8.0) {
|
|
readiness_category = 'automate_now';
|
|
} else if (agentic_readiness_score >= 5.0) {
|
|
readiness_category = 'assist_copilot';
|
|
} else {
|
|
readiness_category = 'optimize_first';
|
|
}
|
|
|
|
const automation_readiness = Math.round(
|
|
agentic_readiness_score * 10
|
|
); // 0-100
|
|
|
|
// Métricas normalizadas 0-100 para el color del heatmap
|
|
const ahtMetric = aht_mean
|
|
? Math.max(
|
|
0,
|
|
Math.min(
|
|
100,
|
|
Math.round(
|
|
100 - ((aht_mean - 240) / 310) * 100
|
|
)
|
|
)
|
|
)
|
|
: 0;
|
|
|
|
const holdMetric = hold_p50
|
|
? Math.max(
|
|
0,
|
|
Math.min(
|
|
100,
|
|
Math.round(
|
|
100 - (hold_p50 / 120) * 100
|
|
)
|
|
)
|
|
)
|
|
: 0;
|
|
|
|
const transferMetric = Math.max(
|
|
0,
|
|
Math.min(
|
|
100,
|
|
Math.round(100 - transfer_rate)
|
|
)
|
|
);
|
|
|
|
// Clasificación por segmento (si nos pasan mapeo)
|
|
let segment: CustomerSegment | undefined;
|
|
if (segmentMapping) {
|
|
const normalizedSkill = skill.toLowerCase();
|
|
if (
|
|
segmentMapping.high_value_queues.some((q) =>
|
|
normalizedSkill.includes(q.toLowerCase())
|
|
)
|
|
) {
|
|
segment = 'high';
|
|
} else if (
|
|
segmentMapping.low_value_queues.some((q) =>
|
|
normalizedSkill.includes(q.toLowerCase())
|
|
)
|
|
) {
|
|
segment = 'low';
|
|
} else {
|
|
segment = 'medium';
|
|
}
|
|
}
|
|
|
|
heatmap.push({
|
|
skill,
|
|
segment,
|
|
volume,
|
|
aht_seconds: aht_mean,
|
|
metrics: {
|
|
fcr: Math.round(globalFcrPct),
|
|
aht: ahtMetric,
|
|
csat: csatMetric0_100,
|
|
hold_time: holdMetric,
|
|
transfer_rate: transferMetric,
|
|
},
|
|
annual_cost,
|
|
variability: {
|
|
cv_aht: Math.round(cv_aht * 100), // %
|
|
cv_talk_time: 0,
|
|
cv_hold_time: 0,
|
|
transfer_rate,
|
|
},
|
|
automation_readiness,
|
|
dimensions: {
|
|
predictability: Math.round(predictability_score * 10) / 10,
|
|
complexity_inverse:
|
|
Math.round(complexity_inverse_score * 10) / 10,
|
|
repetitivity: Math.round(repetitivity_score * 10) / 10,
|
|
},
|
|
readiness_category,
|
|
});
|
|
}
|
|
|
|
console.log('📊 Heatmap backend generado:', {
|
|
length: heatmap.length,
|
|
firstItem: heatmap[0],
|
|
});
|
|
|
|
return heatmap;
|
|
}
|
|
|
|
function computeCsatAverage(customerSatisfaction: any): number | undefined {
|
|
const arr = customerSatisfaction?.csat_avg_by_skill_channel;
|
|
if (!Array.isArray(arr) || !arr.length) return undefined;
|
|
|
|
const values: number[] = arr
|
|
.map((item: any) =>
|
|
safeNumber(
|
|
item?.csat ??
|
|
item?.value ??
|
|
item?.score,
|
|
NaN
|
|
)
|
|
)
|
|
.filter((v) => Number.isFinite(v));
|
|
|
|
if (!values.length) return undefined;
|
|
|
|
const sum = values.reduce((a, b) => a + b, 0);
|
|
return sum / values.length;
|
|
}
|